\(\bullet \)  Capacity. By leveraging the generality of text semantics, the learned discrete codes and code embeddings can effectively capture transferable patterns across different domains. Unlike existing related studies [1]}, [2]},
our approach doesn't directly map the text encodings into item representations, thus it can avoid over-emphasizing the text similarity and is also more robust to the noise from text data. Besides, as discussed in Section 2.2.3, our approach can generate highly distinguishable code representations.
